
Senior Machine Learning / Computer Vision Applied Scientist
We are an innovative, Vancouver-based startup at the forefront of robotics, AI, and machine vision technologies. Backed by VC funding and we’ve been recognized with the 2025 Frost & Sullivan Technology Innovation Leadership Award, the AAM Supplier Excellence Innovation Award, and the 2024 BC Tech “Company of the Year – Growth”, we are on a mission to redefine the future of AI-driven robotic vision systems. Apera AI helps manufacturers make their factories more flexible and productive. Robots enhanced with Apera’s software have 4D Vision – the ability to see and handle objects with human-like capability. Challenging applications such as bin picking, sorting, packaging, and assembly are now open to fast, precise, and reliable automation. Apera is led by an experienced team from high-growth companies focused on robotics, artificial intelligence, and advanced manufacturing.
Job Description:
Robots can do amazing things - if they can see. That’s where you come in. We’re Apera AI. Our breakthrough 4D Vision™ systems turn blind robots into intelligent, adaptable workers. But that transformation doesn’t happen without great teams.
We are looking for a Senior Machine Learning and Computer Vision Applied Scientist with outstanding problem-solving skills to join our AI team. This is an in-person role based in our Vancouver office, where you will collaborate closely with a small group of ambitious engineers and applied scientists delivering the future of robotic perception. Your work will be both grounded in state-of-the-art research and applied to guiding industrial robots on factory floors around the world.
You will own projects end-to-end from literature review through prototyping, benchmarking, and scaling. This includes:
- Extending Apera’s industry best object detection, stereo depth estimation, and 6d pose estimation modules
- Integrating foundation models into our perception stack to improve generalization and few-shot/zero-shot capabilities
- Collaborating with the C++ application team to get models deployed and user experiences optimized
- Collaborating with the infrastructure team to train and manage data on AWS
- Tracking the latest publications in top computer vision and robotics venues
- Prototyping and then implementing production training and deployment, mainly in PyTorch
- Optimizing models for real-time deployment at the edge (~1 Hz, powerful NVIDIA GPU) to support high accuracy, high robustness robot guidance
- Engineering for industrial reliability of 99.9%
About you:
You are motivated, talented, hardworking and have an entrepreneurial spirit. You enjoy making large impact at your company. You enjoy solving challenging problems rooted in real-world physics using science, imagination, creativity, and persistence.
What We’re Looking For:
- STEM bachelor's degree
- 5+ years of experience in using deep learning for CV
- 4+ years of experience in software development
- Bonus:
-
- Master’s or PHD degree with focus on computer vision and machine learning
-
- Experience in 3D geometry and related topics in graphics and deep learning
Why Apera?
- We offer a competitive compensation package, including a base salary range of CAD $160,000–$180,000 plus stock options, designed to recognize your expertise and align your success with the company’s growth.
- You’re building systems that support teams working on real, deployed AI and robotics solutions, not hypothetical future tech.
- A chance to do meaningful, foundational work in a company that values execution and clarity.
- A culture of curiosity: we experiment, learn fast, and share openly.
- Well-funded with a recent Series A to fuel ambitious product roadmaps and growth.
- Trusted by big-name automotive in TIER 1 suppliers.
- High-impact work with autonomy and clear paths to grow.
Join us to make robotics more accessible, predictable, and powerful.
Create a Job Alert
Interested in building your career at Apera AI Inc? Get future opportunities sent straight to your email.
Apply for this job
*
indicates a required field